Abstract
In many networks, different centrality indices reveal conflicting rankings of the nodes. The problem is worsened, if the same nodes occur in different but related network layers, i.e., in multiplex networks. The main concern in the analysis of multiplex networks is maintaining the inherent nature of multiple layers in the explorations. Therefore, in this paper we discuss a method combining a fuzzy operator with a visualization, that allows the exploration of a node’s centrality with respect to different network processes on different layers of the same network simultaneously. Our empirical results indicate that an airport transportation network allows for a smaller number of different behaviors than social networks in a medium sized law firm and a large sized tweet dataset.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abufouda, M., Zweig, K.A.: Interactions around social networks matter: Predicting the social network from associated interaction networks. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 142–145. IEEE/ACM (2014)
Battiston, F., Nicosia, V., Latora, V.: Structural measures for multiplex networks. Physical Review E 89(3), 032,804 (2014)
Borgatti, S.: Centrality and network flow. Social Networks 27(1), 55 – 71 (2005)
Cardillo, A., Gómez-Gardenes, J., Zanin, M., Romance, M., Papo, D., del Pozo, F., Boccaletti, S.: Emergence of network features from multiplexity. Scientific reports 3 (2013)
De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor Scientific Reports 3, 2980 (2013)
De Domenico, M., Solé-Ribalta, A., Omodei, E., Gómez, S., Arenas, A.: Ranking in interconnected multilayer networks reveals versatile nodes. Nature communications 6, 6868 (2015)
Filev, D., Yager, R.R.: Analytic properties of maximum entropy OWA operators. Information Sciences 85(1), 11–27 (1995)
Freeman, L.: Centrality in social network, conceptual clarification. Social Networks 1, 215–239 (1979)
Guimera, R., Mossa, S., Turtschi, A., Amaral, L.A.: The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles. Proceedings of the National Academy of Sciences 102(22), 7794–7799 (2005)
Keeling, M.J., Rohani, P.: Modeling infectious diseases in humans and animals. Princeton University Press (2008)
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nature physics 6(11), 888–893 (2010)
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. Journal of Complex Networks 2(3), 203–271 (2014)
Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Network Analysis - Methodological Foundations, chap. Centrality Indices, pp. 16–60. Springer Verlag (2005)
Koschützki, D., Lehmann, K.A., Tenfelde-Podehl, D., Zlotowski, O.: Network Analysis - Methodological Foundations, chap. Advanced Centrality Concepts, pp. 83–110. Springer Verlag (2005)
Lazega, E.: The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford University Press on Demand (2001)
Solé-Ribalta, A., De Domenico, M., Gómez, S., Arenas, A.: Centrality rankings in multiplex networks. In: Proceedings of the 2014 ACM Conference on Web Science, WebSci ’14, pp. 149–155. ACM, New York, NY, USA (2014)
Tavassoli, S., Zweig, K.A.: Analyzing the activity of a person in a chat by combining network analysis and fuzzy logic. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1565–1568. IEEE/ACM (2015)
Tavassoli, S., Zweig, K.A.: Most central or least central? how much modeling decisions influence a node’s centrality ranking in multiplex networks. arXiv preprint arXiv:1606.05468 (2016)
Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics 18(1), 183–190 (1988)
Yager, R.R.: Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems 11(1), 49–73 (1996)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tavassoli, S., Zweig, K.A. (2017). Analyzing Multiple Rankings of Influential Nodes in Multiplex Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-50901-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50900-6
Online ISBN: 978-3-319-50901-3
eBook Packages: EngineeringEngineering (R0)